Learning-based Decision Support System Research Articles

Enhanced deep learning based decision support system for kidney tumour detection

This study presents a high-accuracy deep learning-based decision support system for kidney cancer detection. The research utilizes a relatively large dataset of 10,000 CT images, including both healthy and tumour-detected kidney scans. After data preprocessing and optimization, various deep learning models were evaluated, with DenseNet-201 emerging as the top performer, achieving an accuracy of 99.75 %. The study compares multiple deep learning architectures, including AlexNet, EfficientNet, Darknet-53, Xception, and DenseNet-201, across different learning rates. Performance metrics such as accuracy, precision, sensitivity, F1-score, and specificity are analysed using confusion matrices. The proposed system outperforms different deep learning networks, demonstrating superior accuracy in kidney cancer detection. The improvement is attributed to effective data engineering and hyperparameter optimization of the deep learning networks. This research contributes to the field of medical image analysis by providing a robust decision support tool for early and rapid diagnosis of kidney cancer. The high accuracy and efficiency of the proposed system make it a promising aid for healthcare professionals in clinical settings.

Predictive Analytics for Demand Forecasting: A deep Learning-based Decision Support System

Demand forecasting is a critical component of supply chain management and business operations, enabling organizations to make informed decisions about production, inventory management, and resource allocation. In recent years, predictive analytics has emerged as a powerful tool for enhancing the accuracy and efficiency of demand forecasting. This review paper explores the transformative role of predictive analytics and deep learning in demand forecasting. It examines how these advanced techniques have evolved from traditional models based on past sales data, offering nuanced predictions through sophisticated statistical and machine learning methods. Deep learning, with its neural network structures, brings automatic feature learning, complex pattern handling, and scalability, enhancing forecasting in sectors like retail, manufacturing, and healthcare. The paper reviews various deep learning models, compares them with traditional methods, and discusses their impact on business operations and decision-making. It concludes by looking at future trends in predictive analytics and deep learning in demand forecasting.

Research on Advance Machine Learning Based Decision Support System for Frauds Detection and Prevention in Online Banking System

Research on Advance Machine Learning Based Decision Support System for Frauds Detection and Prevention in Online Banking System

Machine Learning Based Decision Support System for Coronary Artery Disease Diagnosis

Machine Learning Based Decision Support System for Coronary Artery Disease Diagnosis

Deep Learning Empowered Decision Support Systems for Thyroid Cancer Detection and Management

Exploiting the capability of deep learning-based techniques, this research addresses an important and relevant problem on how to cost effectively detect thyroid cancer. In recent decades, there has been a significant increase in the incidence of thyroid cancer, prompting the need to understand its epidemiology. Existing studies have primarily employed qualitative techniques to investigate a single risk factor correlated with the disease development at a time. However, such an approach is inefficient and tends to ignore the interwoven connections among factors, resulting in a considerable disagreement with the identified risk factors among scholars. Additionally, the use of deep learning techniques in conjunction with medical imaging for computer-aided diagnostic (CAD) systems design has shown promises in detecting the disease, while there are research gaps regarding the detection of subtype and their co-existence. More importantly, existing CAD systems have shortcomings in adapting to different sample groups. To address these challenges, this research aims to shed light on the pathogenesis of thyroid cancer, enhance diagnostic performance, and improve generalisation of deep learning-based decision support systems. By harnessing the power of machine learning, specifically data mining and deep learning techniques, we seek to improve our understanding of the underlying mechanisms of the disease and develop innovative, robust, accurate, and efficient diagnostic tools. Extensive experiments indicate superior performance of the proposed methods than existing works. The systems proposed in this study have great impact to the wider society and contribute to the advancement of thyroid cancer research while enhancing clinical practice in related detection and subsequent management.

Enhancing the medical diagnosis of COVID-19 with learning based decision support systems

Since late December 2019, the COVID-19 pandemic has had substantial impact and long-lasting impact on numerous lives. The surge in patients has overwhelmed hospitals and exhausted essential resources such as masks and gloves. However, in response to this crisis, we have developed a robust solution that can ease the burden on emergency services and manage the influx of patients. Our proposed framework comprises deep learning and machine learning models that can predict and manage patient demand with high accuracy. The first model, is specifically designed to classify computed tomography (CT) scan images for COVID or non-COVID cases. We trained multiple convolutional neural network (CNN) models on a large dataset of CT scan images and evaluated their performance on a separate test set. Our evaluation showed that the ResNet50 model was the most effective, achieving an accuracy of 93.28%. The second model uses patient measurements dataset to predict the likelihood of intensive care unit (ICU) admission for COVID-19 patients. We experimented with the XGBoost machine learning algorithm and found that the accuracy score achieved 88.40%.

From unstructured accident reports to a hybrid decision support system for occupational risk management: The consensus converging approach

Introduction: Workplace accidents in the petroleum industry can cause catastrophic damage to people, property, and the environment. Earlier studies in this domain indicate that the majority of the accident report information is available in unstructured text format. Conventional techniques for the analysis of accident data are time-consuming and heavily dependent on experts’ subject knowledge, experience, and judgment. There is a need to develop a machine learning-based decision support system to analyze the vast amounts of unstructured text data that are frequently overlooked due to a lack of appropriate methodology. Method: To address this gap in the literature, we propose a hybrid methodology that uses improved text-mining techniques combined with an un-bias group decision-making framework to combine the output of objective weights (based on text mining) and subjective weights (based on expert opinion) of risk factors to prioritize them. Based on the contextual word embedding models and term frequencies, we extracted five important clusters of risk factors comprising more than 32 risk sub-factors. A heterogeneous group of experts and employees in the petroleum industry were contacted to obtain their opinions on the extracted risk factors, and the best-worst method was used to convert their opinions to weights. Conclusions and Practical Applications: The applicability of our proposed framework was tested on the data compiled from the accident data released by the petroleum industries in India. Our framework can be extended to accident data from any industry, to reduce analysis time and improve the accuracy in classifying and prioritizing risk factors.

Machine learning-based decision support system for orthognathic diagnosis and treatment planning

BackgroundDento-maxillofacial deformities are common problems. Orthodontic–orthognathic surgery is the primary treatment but accurate diagnosis and careful surgical planning are essential for optimum outcomes. This study aimed to establish and verify a machine learning–based decision support system for treatment of dento-maxillofacial malformations.MethodsPatients (n = 574) with dento-maxillofacial deformities undergoing spiral CT during January 2015 to August 2020 were enrolled to train diagnostic models based on five different machine learning algorithms; the diagnostic performances were compared with expert diagnoses. Accuracy, sensitivity, specificity, and area under the curve (AUC) were calculated. The adaptive artificial bee colony algorithm was employed to formulate the orthognathic surgical plan, and subsequently evaluated by maxillofacial surgeons in a cohort of 50 patients. The objective evaluation included the difference in bone position between the artificial intelligence (AI) generated and actual surgical plans for the patient, along with discrepancies in postoperative cephalometric analysis outcomes.ResultsThe binary relevance extreme gradient boosting model performed best, with diagnostic success rates > 90% for six different kinds of dento-maxillofacial deformities; the exception was maxillary overdevelopment (89.27%). AUC was > 0.88 for all diagnostic types. Median score for the surgical plans was 9, and was improved after human–computer interaction. There was no statistically significant difference between the actual and AI- groups.ConclusionsMachine learning algorithms are effective for diagnosis and surgical planning of dento-maxillofacial deformities and help improve diagnostic efficiency, especially in lower medical centers.

Solving diabetes diagnosis problems using machine learning

This research is devoted to the study of the use of machine learning methods to solve the problem of diagnosing diabetes. The results of using machine learning in the context of diabetes are varied and depend on the methods of data analysis, the models used and the quality of the data provided. Experiments on the Diabetes dataset were conducted in the study using a Naive Bayes classifier model and a linear kernel SVM for a binary classification problem. Models are trained on the training dataset, standardizing features, and evaluated on the test set using confusion, precision, recall, F1-measure, and AUC-ROC metrics. The results obtained confirm that machine learning can improve the accuracy of diagnosing diabetes and classifying its type. This allows for customized treatment plans to be developed, considering the unique characteristics of each patient. Machine learning models are also successful in predicting the likelihood of complications, allowing for preventative measures to be taken. Their use facilitates the integration of data from various sources, enriching patient information. In conclusion, machine learning-based decision support systems assist physicians and patients in making informed decisions.

Machine learning and text mining based real-time semi-autonomous staff assignment system

The growing demand for information systems has significantly increased the workload of consulting and software development firms, requiring them to manage multiple projects simultaneously. Usually, these firms rely on a shared pool of staff to carry out multiple projects that require different skills and expertise. However, since the number of employees is limited, the assignment of staff to projects should be carefully decided to increase the efficiency in job-sharing. Therefore, assigning tasks to the most appropriate personnel is one of the challenges of multiproject management. Assigning a staff to the project by team leaders or researchers is a very demanding process. For this reason, researchers are working on automatic assignment, but most of these studies are done using historical data. It is of great importance for companies that personnel assignment systems work with real-time data. However, a model designed with historical data has the risk of getting unsuccessful results in real-time data. In this study, unlike the literature, a machine learning-based decision support system that works with real-time data is proposed. The proposed system analyses the description of newly requested tasks using textmining and machine-learning approaches and then, predicts the optimal available staff that meets the needs of the project task. Moreover, personnel qualifications are iteratively updated after each completed task, ensuring up-to-date information on staff capabilities. In addition, because our system was developed as a microservice architecture, it can be easily integrated into companies? existing enterprise resource planning (ERP) or portal systems. In a real-world implementation at Detaysoft, the system demonstrated high assignment accuracy, achieving up to 80% accuracy in matching tasks with appropriate personnel.

Development of a Machine Learning Decision Support System for Clinical Flow Cytometry

Abstract High parameter flow cytometry is a highly utilized tool in both research and clinical settings for accurate immunophenotyping in immunology, oncology, and many other disciplines. With increasing dimensionality, manual analysis of data becomes more complex and time consuming. Over the past decade, several automated methods have been developed for cell population clustering and dimensionality reduction of high parameter flow cytometry data which have sped up and simplified the discovery of cell populations not observed by manual gating strategies. However, the input and output of such tools are stochastic in nature, thus making their results difficult to reuse with novel samples such as diagnostic patient samples. Several groups have recently attempted to utilize machine learning algorithms to analyze clinical flow cytometry data. These studies focused on training machine learning models with a goal of predicting a specific diagnosis for each sample. This approach can be limiting when the diagnostic features are not perfectly met to predict a specific diagnosis as well as the absence of the patient's clinical data. To our knowledge, no studies have utilized machine learning to identify and classify individual cells based on their raw fluorescence signature to various cellular populations, whether normal or abnormal, to aid a diagnostician to interpret their significance in a clinical context. In this study, we utilized a data set of ~240 million individual cells from ~9,500 flow cytometry files from 2,300 patients to train a machine learning model to predict cell population classifications as annotated by expert hematopathologists. These annotations were implemented into an end-to-end machine learning based decision support system allowing for rapid, automated analysis of high dimensional flow cytometry data with an output aiding the diagnostician in the generation of a diagnosis. A clinical validation highlighted the robustness of this methodology with 100% sensitivity and 94% specificity in the identification of abnormal B-cell populations on a cohort of 1,500 patients. This decision support system also identifies abnormal T-cell and myeloid cell populations with 100% sensitivity. Overall, the decision support system saves significant time as it removes the need for manual gating and analysis, and aids the diagnostician in identifying discrete cell populations and offering descriptive information about any abnormal populations identified.

GENDER IDENTIFICATION FROM LEFT HAND-WRIST X-RAY IMAGES WITH A HYBRID DEEP LEARNING METHOD

In forensic investigations, characteristics such as gender, age, ethnic origin, and height are important in determining biological identity. In this study, we developed a deep learning-based decision support system for gender recognition from wrist radiographs using 13,935 images collected from individuals aged between 2 and 79 years. Differences in all regions of the images, such as carpal bones, radius, ulna bones, epiphysis, cortex, and medulla, were utilized. A hybrid model was proposed for gender determination from X-ray images, in which deep metrics were combined in appropriate layers of transfer learning methods. Although gender determination from X-ray images obtained from different countries has been reported in the literature, no such study has been conducted in Turkey. It was found that gender discrimination yielded different results for males and females. Gender identification was found to be more successful in females aged between 10 and 40 years than in males. However, for age ranges of 2-10 and 40-79 years, gender discrimination was found to be more successful in males. Finally, heat maps of the regions focused on by the proposed model were obtained from the images, and it was found that the areas of focus for gender discrimination were different between males and females.

Lumbar Spinal Stenosis Analysis with Deep Learning Based Decision Support Systems

Lumbar spinal stenosis (LSS) is a condition that affects the quality of life of the 3 vertebrae, the disc and the canal in the lower back. In this region, the nerves in the canal may be subjected to pressure for various reasons, and disease occurs. Surgical intervention is required to treat canal stenosis, and the exact location and size of the spinal stenosis is critical to the surgery. The UNet model, which is an example of this network, can be further deepened with various deep learning networks. In this study, it will be the basis for creating a system that helps in the diagnosis of spinal stenosis by using a deeper network. The ResUNET model using ResNet as the backbone achieved an average IoU of 0.987. This study demonstrated that expert decision support systems using MR images can be used in the diagnosis of LSS.

Toward Precision Medicine: Development and Validation of A Machine Learning Based Decision Support System for Optimal Sequencing in Castration-Resistant Prostate Cancer.

Selecting a patient-specific sequencing strategy to maximize survival outcomes is a clinically unmet need for patients with castration-resistant prostate cancer (CRPC). We developed and validated an artificial intelligence-based decision support system (DSS) to guide optimal sequencing strategy selection. Clinicopathological data of 46 covariates were retrospectively collected from 801 patients diagnosed with CRPC at 2 high-volume institutions between February 2004 and March 2021. Cox-proportional hazards regression survival (Cox) modeling in extreme gradient boosting (XGB) was used to perform survival analysis for cancer-specific mortality (CSM) and overall mortality (OM) according to the use of abiraterone acetate, cabazitaxel, docetaxel, and enzalutamide. The models were further stratified into first-, second-, and third-line models that each provided CSM and OM estimates for each line of treatment. The performances of the XGB models were compared with those of the Cox models and random survival forest (RSF) models in terms of Harrell's C-index. The XGB models showed greater predictive performance for CSM and OM compared to the RSF and Cox models. C-indices of 0.827, 0.807, and 0.748 were achieved for CSM in the first-, second-, and third-lines of treatment, respectively, while C-indices of 0.822, 0.813, and 0.729 were achieved for OM regarding each line of treatment, respectively. An online DSS was developed to provide visualization of individualized survival outcomes according to each line of sequencing strategy. Our DSS can be used in clinical practice by physicians and patients as a visualized tool to guide the sequencing strategy of CRPC agents.

A machine learning-based decision support system for temporal human cognitive state estimation during online education using wearable physiological monitoring devices

Over the last decade, there has been a considerable increase in the popularity of online education. As a result, the online learning or e-learning industry has flourished, providing benefits to students, learners, educators, and education experts. Despite the advantages of e-learning, it also has its drawbacks. While e-learning enables students to access the learning materials at their convenience from any location, one of the significant challenges is the lack of monitoring of their level of attention during e-learning sessions. It is challenging to ascertain whether a student is actively engaged in the learning process. To address this issue, we have proposed a decision support system (DSS) based on wearable physiological sensor signals (i.e., Electroencephalogram (EEG) signals) that can inform the instructor whether a student is attentive. For developing DSS, we recorded an EEG-based dataset using a neurosky device, and 100 individuals participated in the study. The learning state is divided into two categories: attentive and inattentive. In this paper, machine learning techniques are employed to integrate the proposed DSS, which can predict, analyse, and validate the student’s level of attention or inattention throughout the e-learning session. The findings show that the Support Vector Machine (SVM) approach is the most efficient method for attention prediction, achieving an accuracy of 91.68% compared to logistic regression and ridge regression. Additionally, we examined the frequency bands that were most significant in predicting the learning state, with beta and alpha waves being identified as the key contributors in predicting attention. To further evaluate the data, we use K-means and Hierarchical algorithms to cluster beta and alpha data points. K-means effectively identifies an ideal representative of an attentive or inattentive state. Thus, EEG waves can effectively reveal whether a student is attentive during real-time e-learning sessions, providing a promising approach for providing a valuable tool for decision support in the E-Learning Environment.

Model-contrastive explanations through symbolic reasoning

Explaining how two machine learning classification models differ in their behaviour is gaining significance in eXplainable AI, given the increasing diffusion of learning-based decision support systems. Human decision-makers deal with more than one machine learning model in several practical situations. Consequently, the importance of understanding how two machine learning models work beyond their prediction performances is key to understanding their behaviour, differences, and likeness.Some attempts have been made to address these problems, for instance, by explaining text classifiers in a time-contrastive fashion. In this paper, we present MERLIN, a novel eXplainable AI approach that provides contrastive explanations of two machine learning models, introducing the concept of model-contrastive explanations. We propose an encoding that allows MERLIN to work with both text and tabular data and with mixed continuous and discrete features. To show the effectiveness of our approach, we evaluate it on an extensive set of benchmark datasets. MERLIN is also implemented as a python-pip package.

Bias Analysis in Healthcare Time Series (BAHT) Decision Support Systems from Meta Data.

One of the hindrances in the widespread acceptance of deep learning-based decision support systems in healthcare is bias. Bias in its many forms occurs in the datasets used to train and test deep learning models and is amplified when deployed in the real world, leading to challenges such as model drift. Recent advancements in the field of deep learning have led to the deployment of deployable automated healthcare diagnosis decision support systems at hospitals as well as tele-medicine through IoT devices. Research has been focused primarily on the development and improvement of these systems leaving a gap in the analysis of the fairness. The domain of FAccT ML (fairness, accountability, and transparency) accounts for the analysis of these deployable machine learning systems. In this work, we present a framework for bias analysis in healthcare time series (BAHT) signals such as electrocardiogram (ECG) and electroencephalogram (EEG). BAHT provides a graphical interpretive analysis of bias in the training, testing datasets in terms of protected variables, and analysis of bias amplification by the trained supervised learning model for time series healthcare decision support systems. We thoroughly investigate three prominent time series ECG and EEG healthcare datasets used for model training and research. We show the extensive presence of bias in the datasets leads to potentially biased or unfair machine-learning models. Our experiments also demonstrate the amplification of identified bias with an observed maximum of 66.66%. We investigate the effect of model drift due to unanalyzed bias in datasets and algorithms. Bias mitigation though prudent is a nascent area of research. We present experiments and analyze the most prevalently accepted bias mitigation strategies of under-sampling, oversampling, and the use of synthetic data for balancing the dataset through augmentation. It is important that healthcare models, datasets, and bias mitigation strategies should be properly analyzed for a fair unbiased delivery of service.

A Machine Learning-Based Decision Support System for Predicting and Repairing Cracks in Undisturbed Loess Using Microbial Mineralization and the Internet of Things

Recent years have seen a significant increase in interest across several sectors in the application of learning techniques to extract ground object information, such as soil cracks, from remote sensing high-resolution images. Out of the many technologies, the microbial-induced carbonate deposition (MICP) technology is used to inject bacteria and cementation liquid containing specific bacteria into the cracks of soil to be repaired. Calcium carbonate types of cement are produced by bacterial metabolism so that cracks in the soil could be repaired for disaster management. However, detection of cracks and taking appropriate decisions for repairing are the most fundamental issues that researchers’ attention. Machine learning algorithms can be trained to detect and predict cracks in undisturbed loess using various data sources, such as images captured using the internet of things (IoT), devices, drones, and/or ground-based sensors. These algorithms can be designed to identify different types of cracks based on their shapes, sizes, and orientations, and can be trained on large datasets of labelled crack images to improve their accuracy over time. In this paper, we propose a decision support system (DSS) that detects and predicts cracks and recommends a suitable crack repair methodology. Our results show that our system is highly accurate. Our system provides real-time recommendations to engineers working on crack repair projects in undisturbed loess, guiding them on where and how to apply microbial mineralization treatments based on the predicted crack locations and treatment effectiveness. We noted that the accuracy of the crack detection and prediction can be increased significantly (up to 9.57%) when the proposed DSS approach is considered. Moreover, if PSO is implemented as the optimization model, then we can see that the accuracy can be significantly improved by as much as 21.67% to no DSS approach and 11.32% to the DSS approach.

Increasing Fairness in Predictions Using Bias Parity Score Based Loss Function Regularization

Increasing utilization of machine learning based decision support systems emphasizes the need for resulting predictions to be both accurate and fair to all stakeholders. In this work we present a novel approach to increase a Neural Network model's fairness during training. We introduce a family of fairness enhancing regularization components that we use in conjunction with the traditional binary-cross-entropy based accuracy loss. These loss functions are based on Bias Parity Score (BPS), a score that helps quantify bias in the models with a single number. In the current work we investigate the behavior and effect of these regularization components on bias. We deploy them in the context of a recidivism prediction task as well as on a census-based adult income dataset. The results demonstrate that with a good choice of fairness loss function we can reduce the trained model's bias without deteriorating accuracy even in unbalanced datasets.

Machine learning-based decision support system to distinguish uric acid stones in patients with kidney stones of grey zone Hounsfield units: International multicenter development and external validation study

Machine learning-based decision support system to distinguish uric acid stones in patients with kidney stones of grey zone Hounsfield units: International multicenter development and external validation study